使用手写集数据集编写DBN的代码

以下是使用手写集数据集编写DBN的代码示例：

import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# 定义DBN的参数
n_visible = 784  # 输入层神经元数量
n_hidden1 = 500  # 隐藏层1神经元数量
n_hidden2 = 200  # 隐藏层2神经元数量
n_hidden3 = 50   # 隐藏层3神经元数量
n_classes = 10   # 输出层神经元数量

# 定义输入和输出占位符
x = tf.placeholder(tf.float32, [None, n_visible])
y = tf.placeholder(tf.float32, [None, n_classes])

# 定义权重和偏置变量
weights = {
    'w1': tf.Variable(tf.random_normal([n_visible, n_hidden1])),
    'w2': tf.Variable(tf.random_normal([n_hidden1, n_hidden2])),
    'w3': tf.Variable(tf.random_normal([n_hidden2, n_hidden3])),
    'out': tf.Variable(tf.random_normal([n_hidden3, n_classes]))
}
biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden1])),
    'b2': tf.Variable(tf.random_normal([n_hidden2])),
    'b3': tf.Variable(tf.random_normal([n_hidden3])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

# 定义每一层的输出
def layer(input, weight, bias):
    output = tf.nn.sigmoid(tf.matmul(input, weight) + bias)
    return output

# 定义DBN的前向传播过程
def DBN(x, weights, biases):
    hidden1 = layer(x, weights['w1'], biases['b1'])
    hidden2 = layer(hidden1, weights['w2'], biases['b2'])
    hidden3 = layer(hidden2, weights['w3'], biases['b3'])
    output = tf.matmul(hidden3, weights['out']) + biases['out']
    return output

# 定义损失函数和优化器
pred = DBN(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)

# 定义评估模型的准确率
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# 训练DBN模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(50):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples / 100)
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(100)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, y: batch_ys})
            avg_cost += c / total_batch
        if epoch % 5 == 0:
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
    print("Optimization Finished!")
    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

希望这个代码示例能够帮助你理解如何使用手写集数据集编写DBN的代码。